Abstract: Ethanol is generally used as a therapeutic reagent against Hepatocellular carcinoma (HCC or hepatoma) worldwide, as it can induce Hepatocellular carcinoma cell apoptosis at low concentration through a multifactorial process regulated by several unknown proteins. This paper provides a simple and available proteomic strategy for exploring differentially expressed proteins in the apoptotic pathway. The appropriate concentrations of ethanol required to induce HepG2 cell apoptosis were first assessed by MTT assay, Gisma and fluorescence staining. Next, the central proteins involved in the apoptosis pathway processs were determined using 2D-PAGE, SDS-PAGE, and bio-software analysis. Finally the downregulation of two proteins, AFP and survivin, were determined by immunocytochemistry and reverse transcriptase PCR (RT-PCR) technology. The simple, useful method demonstrated here provides a new approach to proteomic analysis in key bio-regulating process including proliferation, differentiation, apoptosis, immunity and metastasis.
Abstract: One of the main advantages of the LO paradigm is to
allow the availability of good quality, shareable learning material
through the Web. The effectiveness of the retrieval process requires a
formal description of the resources (metadata) that closely fits the
user-s search criteria; in spite of the huge international efforts in this
field, educational metadata schemata often fail to fulfil this
requirement. This work aims to improve the situation, by the
definition of a metadata model capturing specific didactic features of
shareable learning resources. It classifies LOs into “teacher-oriented"
and “student-oriented" categories, in order to describe the role a LO
is to play when it is integrated into the educational process. This
article describes the model and a first experimental validation process
that has been carried out in a controlled environment.
Abstract: 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) catalyzes the conversion of HMG-CoA to mevalonate using NADPH and the enzyme is involved in rate-controlling step of mevalonate. Inhibition of HMGR is considered as effective way to lower cholesterol levels so it is drug target to treat hypercholesterolemia, major risk factor of cardiovascular disease. To discover novel HMGR inhibitor, we performed structure-based pharmacophore modeling combined with molecular dynamics (MD) simulation. Four HMGR inhibitors were used for MD simulation and representative structure of each simulation were selected by clustering analysis. Four structure-based pharmacophore models were generated using the representative structure. The generated models were validated used in virtual screening to find novel scaffolds for inhibiting HMGR. The screened compounds were filtered by applying drug-like properties and used in molecular docking. Finally, four hit compounds were obtained and these complexes were refined using energy minimization. These compounds might be potential leads to design novel HMGR inhibitor.
Abstract: The problem of robust stability and robust stabilization for a class of discrete-time uncertain systems with time delay is investigated. Based on Tchebychev inequality, by constructing a new augmented Lyapunov function, some improved sufficient conditions ensuring exponential stability and stabilization are established. These conditions are expressed in the forms of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. Compared with some previous results derived in the literature, the new obtained criteria have less conservatism. Two numerical examples are provided to demonstrate the improvement and effectiveness of the proposed method.
Abstract: In this paper we propose a family of algorithms based
on 3rd and 4th order cumulants for blind single-input single-output
(SISO) Non-Minimum Phase (NMP) Finite Impulse Response (FIR)
channel estimation driven by non-Gaussian signal. The input signal
represents the signal used in 10GBASE-T (or IEEE 802.3an-2006)
as a Tomlinson-Harashima Precoded (THP) version of random
Pulse-Amplitude Modulation with 16 discrete levels (PAM-16). The
proposed algorithms are tested using three non-minimum phase
channel for different Signal-to-Noise Ratios (SNR) and for different
data input length. Numerical simulation results are presented to
illustrate the performance of the proposed algorithms.
Abstract: This work considered the thermodynamic feasibility
of scrubbing volatile organic compounds into biodiesel in view of
designing a gas treatment process with this absorbent. A detailed
vapour – liquid equilibrium investigation was performed using the
original UNIFAC group contribution method. The four biodiesels
studied in this work are methyl oleate, methyl palmitate, methyl
linolenate and ethyl stearate. The original UNIFAC procedure was
used to estimate the infinite dilution activity coefficients of 13
selected volatile organic compounds in the biodiesels. The
calculations were done at the VOC mole fraction of 9.213x10-8. Ethyl
stearate gave the most favourable phase equilibrium. A close
agreement was found between the infinite dilution activity coefficient
of toluene found in this work and those reported in literature.
Thermodynamic models can efficiently be used to calculate vast
amount of phase equilibrium behaviour using limited number of
experimental data.
Abstract: Based on a non-linear single track model which
describes the dynamics of vehicle, an optimal path planning strategy
is developed. Real time optimization is used to generate reference
control values to allow leading the vehicle alongside a calculated lane
which is optimal for different objectives such as energy consumption,
run time, safety or comfort characteristics. Strict mathematic
formulation of the autonomous driving allows taking decision on
undefined situation such as lane change or obstacle avoidance. Based
on position of the vehicle, lane situation and obstacle position, the
optimization problem is reformulated in real-time to avoid the
obstacle and any car crash.
Abstract: This paper presents a several diagnostic methods designed to electrical machinesespecially for permanent magnets (PM) machines. Those machines are commonly used in small wind and water systems and vehicles drives.Thosemethodsare preferred by the author in periodic diagnostic of electrical machines. The special attentionshould be paid to diagnostic method of turn-to-turn insulation and vibrations. Both of those methodswere createdinInstitute of Electrical Drives and MachinesKomel. The vibration diagnostic method is the main thesis of author’s doctoral dissertation. This is method of determination the technical condition of PM electrical machine basing on its own signals is the subject of patent application No P.405669. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical machines with permanent magnets and there was no method found to determine the technical condition of such machine basing on their own signals.
Abstract: Since 1984 many schemes have been proposed for
digital signature protocol, among them those that based on discrete
log and factorizations. However a new identification scheme based
on iterated function (IFS) systems are proposed and proved to be
more efficient. In this study the proposed identification scheme is
transformed into a digital signature scheme by using a one way hash
function. It is a generalization of the GQ signature schemes. The
attractor of the IFS is used to obtain public key from a private one,
and in the encryption and decryption of a hash function. Our aim is
to provide techniques and tools which may be useful towards
developing cryptographic protocols. Comparisons between the
proposed scheme and fractal digital signature scheme based on RSA
setting, as well as, with the conventional Guillou-Quisquater
signature, and RSA signature schemes is performed to prove that, the
proposed scheme is efficient and with high performance.
Abstract: The multidelays linear control systems described by
difference differential equations are often studied in modern control
theory. In this paper, the delay-independent stabilization algebraic
criteria and the theorem of delay-independent stabilization for linear
systems with multiple time-delays are established by using the
Lyapunov functional and the Riccati algebra matrix equation in the
matrix theory. An illustrative example and the simulation result, show
that the approach to linear systems with multiple time-delays is
effective.
Abstract: Grid computing provides an effective infrastructure for massive computation among flexible and dynamic collection of individual system for resource discovery. The major challenge for grid computing is to prevent breaches and secure the data from trespassers. To overcome such conflicts a semantic approach can be designed which will filter the access requests of peers by checking the resource description specifying the data and the metadata as factual statements. Between every node in the grid a semantic firewall as a middleware will be present The intruder will be required to present an application specifying there needs to the firewall and hence accordingly the system will grant or deny the application request.
Abstract: To investigate the correspondence of theory and
practice, a successfully implemented Knowledge Management
System (KMS) is explored through the lens of Alavi and Leidner-s
proposed KMS framework for the analysis of an information system
in knowledge management (Framework-AISKM). The applied KMS
system was designed to manage curricular knowledge in a distributed
university environment. The motivation for the KMS is discussed
along with the types of knowledge necessary in an academic setting.
Elements of the KMS involved in all phases of capturing and
disseminating knowledge are described. As the KMS matures the
resulting data stores form the precursor to and the potential for
knowledge mining. The findings from this exploratory study indicate
substantial correspondence between the successful KMS and the
theory-based framework providing provisional confirmation for the
framework while suggesting factors that contributed to the system-s
success. Avenues for future work are described.
Abstract: The one-class support vector machine “support vector
data description” (SVDD) is an ideal approach for anomaly or outlier
detection. However, for the applicability of SVDD in real-world
applications, the ease of use is crucial. The results of SVDD are
massively determined by the choice of the regularisation parameter C
and the kernel parameter of the widely used RBF kernel. While for
two-class SVMs the parameters can be tuned using cross-validation
based on the confusion matrix, for a one-class SVM this is not
possible, because only true positives and false negatives can occur
during training. This paper proposes an approach to find the optimal
set of parameters for SVDD solely based on a training set from
one class and without any user parameterisation. Results on artificial
and real data sets are presented, underpinning the usefulness of the
approach.
Abstract: A novel PDE solver using the multidimensional wave
digital filtering (MDWDF) technique to achieve the solution of a 2D
seismic wave system is presented. In essence, the continuous physical
system served by a linear Kirchhoff circuit is transformed to an
equivalent discrete dynamic system implemented by a MD wave
digital filtering (MDWDF) circuit. This amounts to numerically
approximating the differential equations used to describe elements of a
MD passive electronic circuit by a grid-based difference equations
implemented by the so-called state quantities within the passive
MDWDF circuit. So the digital model can track the wave field on a
dense 3D grid of points. Details about how to transform the continuous
system into a desired discrete passive system are addressed. In
addition, initial and boundary conditions are properly embedded into
the MDWDF circuit in terms of state quantities. Graphic results have
clearly demonstrated some physical effects of seismic wave (P-wave
and S–wave) propagation including radiation, reflection, and
refraction from and across the hard boundaries. Comparison between
the MDWDF technique and the finite difference time domain (FDTD)
approach is also made in terms of the computational efficiency.
Abstract: Bovine viral diarrhea virus (BVDV) can cause lifelong
persistent infection. One reason for the phenomena is attributed to
BVDV infection to placenta tissue. However the mechanisms that
BVDV invades into placenta tissue remain unclear. To clarify the
molecular mechanisms, we investigated the possible means that
BVDV entered into bovine trophoblast cells (TPC). Yeast two-hybrid
system was used to identify proteins extracted from TPC, which
interact with BVDV envelope glycoprotein E2. A PGbkt7-E2 yeast
expression vector and TPC cDNA library were constructed. Through
two rounds of screening, three positive clones were identified.
Sequencing analysis indicated that all the three positive clones
encoded the same protein clathrin. Physical interaction between
clathrin and BVDV E2 protein was further confirmed by
coimmunoprecipitation experiments. This result suggested that the
clathrin might play a critical role in the process of BVDV entry into
placenta tissue and might be a novel antiviral target for preventing
BVDV infection.
Abstract: Long term rainfall analysis and prediction is a
challenging task especially in the modern world where the impact of
global warming is creating complications in environmental issues.
These factors which are data intensive require high performance
computational modeling for accurate prediction. This research paper
describes a prototype which is designed and developed on grid
environment using a number of coupled software infrastructural
building blocks. This grid enabled system provides the demanding
computational power, efficiency, resources, user-friendly interface,
secured job submission and high throughput. The results obtained
using sequential execution and grid enabled execution shows that
computational performance has enhanced among 36% to 75%, for
decade of climate parameters. Large variation in performance can be
attributed to varying degree of computational resources available for
job execution.
Grid Computing enables the dynamic runtime selection, sharing
and aggregation of distributed and autonomous resources which plays
an important role not only in business, but also in scientific
implications and social surroundings. This research paper attempts to
explore the grid enabled computing capabilities on weather indices
from HOAPS data for climate impact modeling and change
detection.
Abstract: Data envelopment analysis (DEA) has gained great popularity in environmental performance measurement because it can provide a synthetic standardized environmental performance index when pollutants are suitably incorporated into the traditional DEA framework. Since some of the environmental performance indicators cannot be controlled by companies managers, it is necessary to develop the model in a way that it could be applied when discretionary and/or non-discretionary factors were involved. In this paper, we present a semi-radial DEA approach to measuring environmental performance, which consists of non-discretionary factors. The model, then, has been applied on a real case.
Abstract: In the automotive industry test drives are being conducted
during the development of new vehicle models or as a part of
quality assurance of series-production vehicles. The communication
on the in-vehicle network, data from external sensors, or internal
data from the electronic control units is recorded by automotive
data loggers during the test drives. The recordings are used for fault
analysis. Since the resulting data volume is tremendous, manually
analysing each recording in great detail is not feasible.
This paper proposes to use machine learning to support domainexperts
by preventing them from contemplating irrelevant data and
rather pointing them to the relevant parts in the recordings. The
underlying idea is to learn the normal behaviour from available
recordings, i.e. a training set, and then to autonomously detect
unexpected deviations and report them as anomalies.
The one-class support vector machine “support vector data description”
is utilised to calculate distances of feature vectors. SVDDSUBSEQ
is proposed as a novel approach, allowing to classify subsequences
in multivariate time series data. The approach allows to
detect unexpected faults without modelling effort as is shown with
experimental results on recordings from test drives.
Abstract: The aim of this paper is to adopt a compromise ratio (CR) methodology for fuzzy multi-attribute single-expert decision making proble. In this paper, the rating of each alternative has been described by linguistic terms, which can be expressed as triangular fuzzy numbers. The compromise ratio method for fuzzy multi-attribute single expert decision making has been considered here by taking the ranking index based on the concept that the chosen alternative should be as close as possible to the ideal solution and as far away as possible from the negative-ideal solution simultaneously. From logical point of view, the distance between two triangular fuzzy numbers also is a fuzzy number, not a crisp value. Therefore a fuzzy distance measure, which is itself a fuzzy number, has been used here to calculate the difference between two triangular fuzzy numbers. Now in this paper, with the help of this fuzzy distance measure, it has been shown that the compromise ratio is a fuzzy number and this eases the problem of the decision maker to take the decision. The computation principle and the procedure of the compromise ratio method have been described in detail in this paper. A comparative analysis of the compromise ratio method previously proposed [1] and the newly adopted method have been illustrated with two numerical examples.
Abstract: This purpose of this paper is to develop and validate a
model to accurately predict the cell temperature of a PV module that
adapts to various mounting configurations, mounting locations, and
climates while only requiring readily available data from the module
manufacturer. Results from this model are also compared to results
from published cell temperature models. The models were used to
predict real-time performance from a PV water pumping systems in
the desert of Medenine, south of Tunisia using 60-min intervals of
measured performance data during one complete year. Statistical
analysis of the predicted results and measured data highlight possible
sources of errors and the limitations and/or adequacy of existing
models, to describe the temperature and efficiency of PV-cells and
consequently, the accuracy of performance of PV water pumping
systems prediction models.